A battery-less implantable glucose sensor based on electrical impedance spectroscopy

Explainable Early Prediction of Gestational Diabetes Biomarkers by Combining Medical Background and Wearable Devices: A Pilot Study With a Cohort Group in South Africa

This study aims to explore the potential of Internet of Things (IoT) devices and explainable Artificial Intelligence (AI) techniques in predicting biomarker values associated with GDM when measured 13-16 weeks prior to diagnosis. We developed a system that forecasts biomarkers such as LDL, HDL, triglycerides, cholesterol, HbA1c, and results from the Oral Glucose Tolerance Test (OGTT) including fasting glucose, 1-hour, and 2-hour post-load glucose values. These biomarker values are predicted based on sensory measurements collected around week 12 of pregnancy, including continuous glucose levels, short physical movement recordings, and medical background information. To the best of our knowledge, this is the first study to forecast GDM-associated biomarker values 13 to 16 weeks prior to the GDM screening test, using continuous glucose monitoring devices, a wristband for activity detection, and medical background data. We applied machine learning models, specifically Decision Tree and Random Forest Regressors, along with Coupled-Matrix Tensor Factorisation (CMTF) and Elastic Net techniques, examining all possible combinations of these methods across different data modalities. The results demonstrated good performance for most biomarkers. On average, the models achieved Mean Squared Error (MSE) between 0.29 and 0.42 and Mean Absolute Error (MAE) between 0.23 and 0.45 for biomarkers like HDL, LDL, cholesterol, and HbA1c. For the OGTT glucose values, the average MSE ranged from 0.95 to 2.44, and the average MAE ranged from 0.72 to 0.91. Additionally, the utilisation of CMTF with Alternating Least Squares technique yielded slightly better results (0.16 MSE and 0.07 MAE on average) compared to the well-known Elastic Net feature selection technique. While our study was conducted with a limited cohort in South Africa, our findings offer promising indications regarding the potential for predicting biomarker values in pregnant women through the integration of wearable devices and medical background data in the analysis. Nevertheless, further validation on a larger, more diverse cohort is imperative to substantiate these encouraging results.

Survey on Energy Harvesting for Biomedical Devices: Applications, Challenges and Future Prospects for African Countries

Self-powered biomedical devices, which are the new vision of Internet Of Things (IOT) healthcare, are facing many technical and application challenges. Many research works have reported biomedical devices and self-powered applications for healthcare, along with various strategies to improve the monitoring time of self-powered devices or to eliminate the dependence on electrochemical batteries. However, none of these works have especially assessed the development and application of healthcare devices in an African context. This article provides a comprehensive review of self-powered devices in the biomedical research field, introduces their applications for healthcare, evaluates their status in Africa by providing a thorough review of existing biomedical device initiatives and available financial and scientific cooperation institutions in Africa for the biomedical research field, and highlights general challenges for implementing self-powered biomedical devices and particular challenges related to developing countries. The future perspectives of the aforementioned research field are provided, as well as an architecture for improving this research field in developing countries.

Optimized Copper-Based Microfeathers for Glucose Detection

Diabetes is expected to rise substantially by 2045, prompting extensive research into accessible glucose electrochemical sensors, especially those based on non-enzymatic materials. In this context, advancing the knowledge of stable metal-based compounds as alternatives to non-enzymatic sensors becomes a scientific challenge. Nonetheless, these materials have encountered difficulties in maintaining stable responses under physiological conditions. This work aims to advance knowledge related to the synthesis and characterization of copper-based electrodes for glucose detection. The microelectrode presented here exhibits a wide linear range and a sensitivity of 1009 µA∙cm-2∙mM-1, overperfoming the results reported in literature so far. This electrode material has also demonstrated outstanding results in terms of reproducibility, repeatability, and stability, thereby meeting ISO 15197:2015 standards. Our study guides future research on next-generation sensors that combine copper with other materials to enhance activity in neutral media.